Research on Consumers’ Perceived Value of Online Garment Customisation

Through a literature review, the factors affecting consumers’ perceived value of online garment customisation are summarised, which are divided into eight dimensions: quality value, price value, service performance, website function, personalised demand, emotional value, social value and supply chain factors. In order to explore the influence of each dimension on consumers’ purchasing behaviour, this paper puts forward a research model framework (Figure 1).

Fig. 1

Before the questionnaire was formally issued, interviews were conducted with professional teachers and custom consumers in garment related fields. Inaccurate expressions and semantic duplication were eliminated, and the study finally set 34 measurement elements. The final measurement elements and codes are shown in Table 1.

Based on the theoretical model constructed in this paper, a questionnaire was designed. The survey questionnaire included the elements of all dimensions in Table 1. According to the respondents’ recognition of each element of each dimension and the degree to which they consider them important, each element was analysed to extract the dimension of customer perceived value.

The questionnaire was issued online to the target consumer and potential consumer groups customised online. Therefore, the research method adopted in this paper was the online issuance of questionnaires. The questionnaires were distributed and collected through a website called “Questionnaire Star”. The survey was conducted in Xi’an, Shaanxi, China, and adopted intentional sampling in non-conceptual sampling close to the target customers of online customisation. The interviewees were 20–55 year-old consumers with a college degree or above, and the survey time was from October 15, 2021, to October 19, 2021.

The questionnaire was evaluated by the Likert 5 scale method, where 1 represents very insignificant, 5 represents very important, with 1–5 representing the degree of importance in turn. A total of 362 questionnaires were collected. The questionnaires were screened through the answer time and answer situation. After removing 41 invalid questionnaires that did not meet the requirements of the questionnaire, 321 valid ones were obtained. The effective recovery rate was 88.67%.

The final data collected were imported into SPSS statistical software, and 321 valid questionnaires were statistically analysed. First, descriptive analysis was carried out, the results of which are shown in Table 2. All the mean values in the table are greater than 3, indicating that the 34 elements have a certain influence on consumers’ purchase behaviour towards online customised garments.

In order to clarify the impact of each element on consumers’ perceived value, factor analysis was conducted on the data. Using SPSS software for reliability analysis, the Cronbach Alpha coefficient was found to be 0.932, and the result was greater than 0.8. This indicates that the reliability of the questionnaire is good. Kaiser-Meyer-Olkin (KMO) and Bartlett tests were performed on the data. The KMO value was 0.906, which was greater than 0.5. The Bartlett test chi-square value was 5620.012, the degrees of freedom were 561, and P was 0.000, indicating that the data were suitable for factor analysis.

Common factors were extracted by principal component analysis, and 5 common factors with characteristic values greater than 1 were extracted. The cumulative explanatory variable was 68.225%, as shown in Table 3. In order to understand the components of the five common factors, this paper used the maximum variance method to perform Kaiser normalised orthogonal rotation on the factors. The factor loading after rotation reflects the relationship between the factor and each element. The higher the load, the greater the correlation. The rotation converges after 9 iterations.

In the process of factor analysis, when the load is greater than or equal to 0.5, it can be considered that the factor can represent the indicator variable. The loads of PRI2, SER1, IND5, PRI3 and SUP1 are less than 0.5, which means that these five elements cannot dominate the indicator variables. Therefore, the good value for money, service attitude, self-selection of accessories, discount intensity and production cycle were excluded from the scale. The final rotation component matrix is shown in Table 4.

Names of the five common factors:

• The first factor: website comprehensive function (F₁). This factor has a large load in providing comprehensive information, convenient operation, payment security, merchant reputation, logistics networking, interface aesthetics, logistics speed, etc. In comprehensive consideration, the first factor is defined as the website comprehensive function.

• The second factor: personalized service (F₂). This factor has a large load in providing measure body size at home, online one-to-one customer service, style recommendation service, exclusive after-sales service, participation and dress for your body type is available. Therefore, this factor is defined as personalised service.

• The third factor: product quality (F₃). The load of this factor is mainly in the quality of accessories, comfort, craftsmanship, wear resistance and ease to take care of. This factor is defined as product quality.

• The fourth factor: emotional demand (F₄). The load of the commodity attribute factor is mainly on the following four variables: curiosity driven, pursuing individuality, bringing happiness and having exclusive identification. This factor is defined as emotional demand.

• The fifth factor: social value (F₅). This factor reflects information on the following three variables: symbolising status, obtaining social recognition and addressing occasion needs. It has a large load on these three variables. Therefore, this factor is defined as social value.

In summary, factor analysis removed the original 34 measurement variables, retained 29 measurement variables, and extracted five common factors. According to the results of factor analysis, five common factors were redefined, which are website comprehensive function, personalised service, product quality, emotional demand and social value.

With the help of some concepts of fuzzy mathematics, fuzzy comprehensive evaluation allows to evaluate the actual comprehensive evaluation problems.

Based on fuzzy mathematics, it quantifies some fuzzy factors and makes a comprehensive evaluation [17]. In order to test consumers’ comprehensive attitude towards the research results of the evaluation scale proposed, the fuzzy comprehensive evaluation method was used to make a reasonable evaluation.

First, set the universe of discourse: U={u1,u2,…un}, {\rm{U}} = \{{{\rm{u}}_1},{{\rm{u}}_2}, \ldots {{\rm{u}}_{\rm{n}}}\},

Sort the elements in U and publish m comments as: V={v1,v2,…vm}. {\rm{V}} = \{{{\rm{v}}_1},{{\rm{v}}_2}, \ldots {{\rm{v}}_{\rm{m}}}\}.

Where V_I is the i-th evaluation sequence, that is, a sorting of elements in U.

Let A be the fuzzy subset of the importance of factors in U, and R the fuzzy relation matrix from U to V derived from fuzzy value mapping f. Through the fuzzy linear transformation of R, A can be converted into a comprehensive evaluation subset for V B=A*R={b1,b2,…,bm} {\rm{B}} = {\rm{A}}*{\rm{R}} = \{{{\rm{b}}_1},{{\rm{b}}_2}, \ldots,{{\rm{b}}_{\rm{m}}}\}

According to the comprehensive score of each evaluation object calculated, the comprehensive attitude of consumers can be obtained.

There are many factors involved in this paper. Factor set U can be divided into n subsets according to the number of factors obtained by factor analysis, which are recorded as U₁, U₂, ..., U_n, where n = 5, U_i={F₁, F₂, F₃, F₄, F₅}. In this study, the weight of each subset is equal, that is, A is the same.

Analyze the results of the factor analysis in the previous chapter. Taking the first factor as an example, the weight judgment matrix R₁ is constructed according to the above data, and the judgment result B₁ is obtained. R1=[00.0190.1280.4210.43300.0160.1150.4700.39900.0090.0720.2960.62300.0090.1030.3210.5670.0030.0090.1430.4450.4020.0030.0060.0970.4670.4270.0030.0090.1150.4670.4050.0030.0190.1430.4420.3930.0030.0310.2590.4450.26200.0120.2020.4360.349]A1=(1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10) \matrix{{{{\rm{R}}_1} = \left[ {\matrix{0 & {0.019} & {0.128} & {0.421} & {0.433} \cr 0 & {0.016} & {0.115} & {0.470} & {0.399} \cr 0 & {0.009} & {0.072} & {0.296} & {0.623} \cr 0 & {0.009} & {0.103} & {0.321} & {0.567} \cr {0.003} & {0.009} & {0.143} & {0.445} & {0.402} \cr {0.003} & {0.006} & {0.097} & {0.467} & {0.427} \cr {0.003} & {0.009} & {0.115} & {0.467} & {0.405} \cr {0.003} & {0.019} & {0.143} & {0.442} & {0.393} \cr {0.003} & {0.031} & {0.259} & {0.445} & {0.262} \cr 0 & {0.012} & {0.202} & {0.436} & {0.349} \cr}} \right]} \hfill \cr {{{\rm{A}}_1} = (1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10,1/10)} \hfill \cr} then B1=A1*R1=0.0012,0.0139,0.1377,0.421,0.426) \matrix{{{{\bf{B}}_1} = {{\bf{A}}_1}} \cr {*{{\bf{R}}_1} = 0.0012,0.0139,0.1377,0.421,0.426)} \cr}

The results show that 0.12% of consumers think that the comprehensive function of the website is very unimportant for perceived value, 1.39% that it is not important, 13.77% that it is general, 42.1% that it is important, and 42.6% that it is very important. According to the principle of maximum subordination, the comprehensive function of the network is “very important”.

Similarly, fuzzy comprehensive evaluation was carried out for the other four factors, the results of which are shown in Table 5.

The results show that consumers generally believe that the comprehensive function of the network is very important, and the data ratio of the comprehensive function of the website is 84.70%. These data fully illustrate that the comprehensive function of the website is an important part of consumers’ perceived value. In the investigation of the quality value, the important data ratio of product quality reached 87.48%. Among the other three factors, according to the principle of maximum subordination, it is considered that these three factors are important. The important proportions are personalized service 35.47%, emotional demand 36.35% and social value 41.40%, respectively. It shows that consumer recognition of these three factors is an integral part of their perceived value. This research results can be used to evaluate the perceived value of online garment customisation consumers.